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Paper Citation Record · LEDGER

Dataset Pruning: Reducing Training Data by Examining Generalization Influence

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2205.09329.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2205.09329 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:09:23.472912Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T20:40:07.609687Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5af602d4-11a0-4e8e-99fb-e88dfa57fc1e · inbound

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 168

Resolution
verified exact
arxiv_id, observed 2026-05-16T17:56:23.605564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-16T17:56:23.281678Z digest=sha256:559decc46e67a023ea827568de7b38f2c0cbb4561494e28b6a3ccad5d150e064

Observation df5cb2ad-f2cc-43f7-9a4b-a007ba840dea · inbound

Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality cites this paper.

Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-19T04:42:58.708905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T04:42:33.911146Z digest=sha256:468f625a7f1243e6b779dfed53d8540bb5516887f7295c2b8b6cd0b5945eb539

Observation c2a8d3a5-f29c-4e6c-a723-76737d06d775 · inbound

Influence Functions for Preference Dataset Pruning cites this paper.

Influence Functions for Preference Dataset Pruning Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.472912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.472912Z digest=sha256:8d8d9061e0f0c71385ed69128ff9ef6c20fc745dc06faabed0207bfa57377c3a

Observation 6bad427f-3841-44f5-adf0-b9a80d7127f9 · inbound

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models cites this paper.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:17.849316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:17.849316Z digest=sha256:9be75cab13b71733e348e6184892e8b857e63b0621d4f539bde14257e4f43e41

Observation d068dc37-bb0b-4770-84c9-ff61f9175b1c · inbound

Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data cites this paper.

Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:21:23.867469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T13:20:56.418690Z digest=sha256:4d8a33df4e32d43990c391d498a2c86016d978e97d4bf9e584d6d0652e534a65

Observation d859c6a1-2d00-492d-8768-0045a8ede3f6 · inbound

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation cites this paper.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-03T02:34:21.345501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:34:21.345501Z digest=sha256:3ee69fdddb08d4333c7c9dc2bd746167d9c415f98ca17014bcf26996998ffb87

Observation 88deb570-5f9d-4490-8764-cfefe56d70e9 · inbound

Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks cites this paper.

Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:06:03.408797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T15:38:50.333310Z digest=sha256:c49f8a1d3883f4027cf4e1dcc1788cc89948e259aaa723ac3a6d07dbb7e90953

Observation 84a19e91-de4a-4ec0-8b46-c99377ef5dcd · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 140

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:06:19.167225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-10T06:01:46.885030Z digest=sha256:bf9b62442254d961cce5d05cd544dfd10451fc85ed0dfbca56a4758df33c3b48

Observation 91b0046e-0b63-4bcf-ba0c-75be0c970af0 · inbound

SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning cites this paper.

SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-06-30T22:05:05.675799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T22:02:30.217607Z digest=sha256:dc2a9e3062bb8c36615bbd2a72619c708590a89d65d6c09d6792ef02c54643e2

Observation 471f9fed-a8b5-4b85-bafc-938fed04291b · inbound

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework cites this paper.

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 127

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:07:27.179150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T18:26:32.883834Z digest=sha256:3b70e431c7a20f1b63a3bcdd26db67de83c565532d444ed8e1f3426d11c0c5f6

Observation 56b5d1f3-6972-48fb-b2fb-3f289b6ef74b · inbound

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation cites this paper.

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:40:07.611636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-25T19:58:21.335371Z digest=sha256:ba26e3834b71cba272bf3adb8cdf55bc20e63a8b1675e7c544eb52aa918d6660